Monocular vision with deep neural networks for autonomous mobile robots navigation

Abstract

Enabling mobile robots to explore the formerly unidentified environment is a challenging task. The current paper describes the internal analysis algorithm for mobile robots that combines various convolutional neural network (CNN) layers with the decision-making process in a hierarchical way. The whole system is trained end-to-end on data captured by a low-cost depth camera (RGB-D). The output consists of the proposed expansion model of the robot's critical moving directions to achieve autonomous analysis ability. Training this model through the dataset is created using Hand-Controlled Mobile Robot (HCMR) built for this purpose. The experiments were conducted by moving this robot in natural and diverse environments. The robot was trained using this data and applied for environmental investigation decisions (the control labels) using CNN to enable the robot to automatically sense the navigation without a map in an unknown environment. Furthermore, extensive experiments were conducted indoors and attained an accuracy of 77%. Experiments showed that the proposed model was able to reach equivalent results that are generally obtained enormously from an expensive sensor. In addition, comprehensive comparisons were drawn between the human-controlled robot and a robot trained using a deep learning process to determine decisions to control the robot's movement. The reached results were identical and satisfactory.